I want to use KPCA on large data for dimensional reduction. As I know, a large data set leads to a large K, and storing K may become a problem. One way to deal with this is to perform clustering on the dataset, and populate the kernel with the means of those clusters.
So, How can I calculate kernel by performing clustering on the datset? How can I populate the kernel with the means of those clusters?
Reshi Nawab
For your question , you can readout this :
Clustering large datasets with kernel methods
Conference Paper Clustering large datasets with kernel methods
Full text is available
. I hope you get your answer , also you can text the authors for better clarity.
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